We can build a recommendation engine suited to your customer preferences. Leveraging sophisticated machine learning algorithms
we can predict which of your products will be more appealing for
your customers.
Our recommendation engine can foster product cross-selling by simply helping your customers to explore and discover new products they will like.

Challenge

Luxury beauty brand Coty wanted us to come up with an innovative data-driven recommendation engine for fragrances.

Solution

We devised a survey of 200 questions to gather psychometric data about a representative sample of the UK population.
The data collected gave us the raw material to:

  • Cluster the population into 12 personas
  • Define six rating rules to quantify how much a customer likes a perfume
  • Design five recommendation engines working in symbiosis

We used machine learning to formulate 18 simple, effective questions for Coty’s customers.
Using an iPad app, the questionnaire was put to the test in Luton airport and Oxford Street for three days with real Coty customers.


82.3% of respondents indicated that
they liked their recommendations,

giving high scores of 4/5

The first trials generated a buzz.
Some of the customer feedback were:

I love it! | Flamboyant! | Reminds me of university, positive memory, my sister wears it! | Just perfect! Very smooth and pleasant. | Own it, love it!

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